Managing server energy and reducing operational cost for online service providers

The past decade has seen the energy consumption in servers and Internet Data Centers (IDCs) skyrocket. A recent survey estimated that the worldwide spending on servers and cooling have risen to above $30 billion and is likely to exceed spending on the new server hardware . The rapid rise in energy consumption has posted a serious threat to both energy resources and the environment, which makes green computing not only worthwhile but also necessary. This dissertation intends to tackle the challenges of both reducing the energy consumption of server systems and by reducing the cost for Online Service Providers (OSPs). Two distinct subsystems account for most of IDC’s power: the server system, which accounts for 56% of the total power consumption of an IDC, and the cooling and humidifcation systems, which accounts for about 30% of the total power consumption. The server system dominates the energy consumption of an IDC, and its power draw can vary drastically with data center utilization. In this dissertation, we propose three models to achieve energy effciency in web server clusters: an energy proportional model, an optimal server allocation and frequency adjustment strategy, and a constrained Markov model. The proposed models have combined Dynamic Voltage/Frequency Scaling (DV/FS) and Vary-On, Vary-off (VOVF) mechanisms that work together for more energy savings. Meanwhile, corresponding strategies are proposed to deal with the transition overheads. We further extend server energy management to the IDC’s costs management, helping the OSPs to conserve, manage their own electricity cost, and lower the carbon emissions. We have developed an optimal energy-aware load dispatching strategy that periodically maps more requests to the locations with lower electricity prices. A carbon emission limit is placed, and the volatility of the carbon offset market is also considered. Two energy effcient strategies are applied to the server system and the cooling system respectively. With the rapid development of cloud services, we also carry out research to reduce the server energy in cloud computing environments. In this work, we propose a new live virtual machine (VM) placement scheme that can effectively map VMs to Physical Machines (PMs) with substantial energy savings in a heterogeneous server cluster. A VM/PM mapping probability matrix is constructed, in which each VM request is assigned with a probability running on PMs. The VM/PM mapping probability matrix takes into account resource limitations, VM operation overheads, server reliability as well as energy effciency. The evolution of Internet Data Centers and the increasing demands of web services raise great challenges to improve the energy effciency of IDCs. We also express several potential areas for future research in each chapter.

An ethnobotanical survey of the economic and cultural significance of non-timber forest products in the southwest Rhodope Mountain region of Bulgaria

The people of the southwestern Rhodope Mountains of Bulgaria live in small, mountainous villages and rural areas. They rely on berries, herbs, and mushrooms provided by the forest and maintain a lifestyle and culture of gathering them. This study determined the economic and landscape concentration of Non-Timber Forest Products (NTFPs) and how this has changed in the past twenty years in the region of Garmen. The objective was to gauge the cultural and economic significance of NTFPs in the lives of the people who live there. Data was collected using informal, open-ended interviews and through participant observation. Results indicate that ethnicity influence how resources are utilized. Roma people collect mushrooms for income generation; Orthodox Bulgarians gather herbs, berries, and mushrooms for medicinal purposes, to supplement their diets, and to carry on traditions. Bulgarian Muslims collect for a combination of the aforementioned reasons. Changes that occur in the forests affect each of the ethnic groups in different ways and forest management practices should include people’s knowledge and uses of NTFPs.

Analysis of atmospheric transport and the effects of seasonal and interannual transport variability on measurements made at the Pico Mountain observatory

A non-hierarchical K-means algorithm is used to cluster 47 years (1960–2006) of 10-day HYSPLIT backward trajectories to the Pico Mountain (PM) observatory on a seasonal basis. The resulting cluster centers identify the major transport pathways and collectively comprise a long-term climatology of transport to the observatory. The transport climatology improves our ability to interpret the observations made there and our understanding of pollution source regions to the station and the central North Atlantic region. I determine which pathways dominate transport to the observatory and examine the impacts of these transport patterns on the O3, NOy, NOx, and CO measurements made there during 2001–2006. Transport from the U.S., Canada, and the Atlantic most frequently reaches the station, but Europe, east Africa, and the Pacific can also contribute significantly depending on the season. Transport from Canada was correlated with the North Atlantic Oscillation (NAO) in spring and winter, and transport from the Pacific was uncorrelated with the NAO. The highest CO and O3 are observed during spring. Summer is also characterized by high CO and O3 and the highest NOy and NOx of any season. Previous studies at the station attributed the summer time high CO and O3 to transport of boreal wildfire emissions (for 2002–2004), and boreal fires continued to affect the station during 2005 and 2006. The particle dispersion model FLEXPART was used to calculate anthropogenic and biomass-burning CO tracer values at the station in an attempt to identify the regions responsible for the high CO and O3 observations during spring and biomass-burning impacts in summer.